In recent years, computational identification of immunogenic regions/segments in a given protein antigen has provided increasing assistance in guiding the experimental validation. Since the majority of the epitope area was dominated by discontinuous amino acids in the surface of protein antigens, a lot of efforts have been devoted into computing spatial/conformational epitopes based on protein structures. These methods can be roughly divided into two tracks, one of which proposing useful parameters to discriminate epitope residues from common surface ones, while the other focusing on various classification algorithm to improve the performance.

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Allows the prediction and visualization of antibody epitopes in a given protein sequence or structure. As ElliPro is based on the geometrical properties of protein structure and does not require training, it might be more generally applied for predicting different types of protein-protein interactions.

Predicts discontinuous B cell epitopes from protein three dimensional structures. The method utilizes calculation of surface accessibility (estimated in terms of contact numbers) and a novel epitope propensity amino acid score. The final scores are calculated by combining the propensity scores of residues in spatial proximity and the contact numbers.

A web-based tool that aims to predict immunogenic regions in either a protein three-dimensional structure or a linear sequence. Epitopia implements a machine-learning algorithm that was trained to discern antigenic features within a given protein. The Epitopia algorithm has been compared to other available epitope prediction tools and was found to have higher predictive power. A special emphasis was put on the development of a user-friendly graphical interface for displaying the results.

Prediction of antigenic epitopes on protein surfaces by consensus scoring. The energy function called EMPIRE (EMpirical Protein-InteRaction Energy), when coupled with a refinement strategy, is found to provide a significantly improved success rate in near native selections when applied to RosettaDock and refined ZDOCK docking decoys.

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